Analytical Processing - Explore the Science & Experts | ideXlab

Scan Science and Technology

Contact Leading Edge Experts & Companies

Analytical Processing

The Experts below are selected from a list of 27822 Experts worldwide ranked by ideXlab platform

Philip S Yu – 1st expert on this subject based on the ideXlab platform

  • LOCUST: An Online Analytical Processing Framework for High Dimensional Classification of Data Streams
    2008 IEEE 24th International Conference on Data Engineering, 2008
    Co-Authors: Charu C. Aggarwal, Philip S Yu

    Abstract:

    In recent years, data streams have become ubiquitous because of advances in hardware and software technology. The ability to adapt conventional mining problems to data streams is a great challenge in a data stream environment. Many data streams are inherently high dimensional, which creates a special challenge for data mining algorithms. In this paper, we consider the problem of classification of high dimensional data streams. For the high dimensional case, even traditional classifiers do not work very well on fixed data sets. We discuss a number of insights for the intractability of the high dimensional case. We use these insights to propose a new classification method (LOCUST) which avoids many of these weaknesses. The key is to develop a subspace-based instance centered classification approach which can be implemented efficiently for a fast data stream. We propose a methodology to effectively process the data stream in an organized way, so that the intermediate data structures can be used to sample locally discriminative subspaces for the classification process. We show that LOCUST is able to work effectively in the high dimensional case, and is also flexible in terms of increased robustness with greater resource availability.

  • Graph OLAP: Towards online Analytical Processing on graphs
    Proceedings – IEEE International Conference on Data Mining, ICDM, 2008
    Co-Authors: Chen Chen, Xifeng Yan, Feida Zhu, Jia Wei Han, Philip S Yu

    Abstract:

    OLAP (On-Line Analytical Processing) is an important notion in data analysis. Recently, more and more graph or networked data sources come into being. There exists a similar need to deploy graph analysis from different perspectives and with multiple granularities. However, traditional OLAP technology cannot handle such demands because it does not consider the links among individual data tuples. In this paper, we develop a novel graph OLAP framework, which presents a multi-dimensional and multi-level view over graphs. The contributions of this work are two-fold. First, starting from basic definitions, i.e., what are dimensions and measures in the graph OLAP scenario, we develop a conceptual framework for data cubes on graphs. We also look into different semantics of OLAP operations, and classify the framework into two major subcases: informational OLAP and topological OLAP. Then, with more emphasis on informational OLAP (topological OLAP will be covered in a future study due to the lack of space), we show how a graph cube can be materialized by calculating a special kind of measure called aggregated graph and how to implement it efficiently. This includes both full materialization and partial materialization where constraints are enforced to obtain an iceberg cube. We can see that the aggregated graphs, which depend on the graph properties of underlying networks, are much harder to compute than their traditional OLAP counterparts, due to the increased structural complexity of data. Empirical studies show insightful results on real datasets and demonstrate the efficiency of our proposed optimizations.

Kedar Sambhoos – 2nd expert on this subject based on the ideXlab platform

  • Data association and graph Analytical Processing of hard and soft intelligence data
    Proceedings of the 16th International Conference on Information Fusion, 2013
    Co-Authors: Ketan Date, Sushama Khopkar, G A Gross, Richie Nagi, Kedar Sambhoos

    Abstract:

    In traditional data fusion hard physical sensor data has been the main source of information. This has changed during the past decade, under the backdrop of counter insurgency (COIN). In the COIN environment the majority of information comes from human sources (soft data). The source of this information can be human informants or soldiers conducting reconnaissance in the field. This human sourced soft data is filled with vast amounts of valuable information. Recently a large number of Natural Language Processing techniques have been developed to process this soft data into the form of relational graphs. In this paper we have described various graph Analytical techniques that can be applied towards fusion of hard and soft information and understanding the situations of interest by an analyst. The Processing elements exhibited in this paper are association of entities and relations in observational hard and soft data graphs to form the cumulative data graph, situation assessment via graph matching of situations of interest against the cumulative data graph, and social network analysis to identify and extract high value individuals in the network. To illustrate these graph analytic tools we have used the Sunni message thread of SYNCOIN consisting of 114 soft messages and 4 hard data reports. The value of this work has been demonstrated with detailed analysis and examples from the aforementioned dataset.

  • Data association and graph Analytical Processing of hard and soft intelligence data
    Information Fusion (FUSION) 2013, 2013
    Co-Authors: Kazuyuki Date, G A Gross, Sushama Khopkar, Richie Nagi, Kedar Sambhoos

    Abstract:

    In traditional data fusion hard physical sensor data has been the main source of information. This has changed during the past decade, under the backdrop of counter insurgency (COIN). In the COIN environment the majority of information comes from human sources (soft data). The source of this information can be human informants or soldiers conducting reconnaissance in the field. This human sourced soft data is filled with vast amounts of valuable information. Recently a large number of Natural Language Processing techniques have been developed to process this soft data into the form of relational graphs. In this paper we have described various graph Analytical techniques that can be applied towards fusion of hard and soft information and understanding the situations of interest by an analyst. The Processing elements exhibited in this paper are association of entities and relations in observational hard and soft data graphs to form the cumulative data graph, situation assessment via graph matching of situations of interest against the cumulative data graph, and social network analysis to identify and extract high value individuals in the network. To illustrate these graph analytic tools we have used the Sunni message thread of SYNCOIN consisting of 114 soft messages and 4 hard data reports. The value of this work has been demonstrated with detailed analysis and examples from the aforementioned dataset. © 2013 ISIF ( Intl Society of Information Fusi.

J. Ertlschweiger – 3rd expert on this subject based on the ideXlab platform

  • A prototype metadata database for online Analytical Processing of environmental data
    Proceedings. Ninth International Conference on Scientific and Statistical Database Management (Cat. No.97TB100150), 1997
    Co-Authors: H. Geller, Sue Conger, J. Ertlschweiger

    Abstract:

    We present preliminary results on the development of a prototype database system demonstrating the utility of the integration of environmental metadata within an online Analytical Processing environment. We utilized existing data derived from CD-ROMs of the National Snow and Ice Data Center (NSIDC), the Consortium for International Earth Science Information Network (CIESIN) and the US Geological Survey (USGS). We populated a prototype metadata database whose architecture facilitates the scientific and statistical investigations of geophysical parameters associated with the polar regions, allowing for data fusion from other regions and Earth science disciplines, facilitating interdisciplinary studies. The user can extract information combining the knowledge of two disparate sources of geophysical data to allow a query that would result in a useful product. Furthermore, we demonstrate the utility of allowing access to this database via the World Wide Web using an interface to the underlying Oracle database management system.

  • SSDBM – A prototype metadata database for online Analytical Processing of environmental data
    Proceedings. Ninth International Conference on Scientific and Statistical Database Management (Cat. No.97TB100150), 1997
    Co-Authors: Harold A. Geller, Sue Conger, J. Ertlschweiger

    Abstract:

    We present preliminary results on the development of a prototype database system demonstrating the utility of the integration of environmental metadata within an online Analytical Processing environment. We utilized existing data derived from CD-ROMs of the National Snow and Ice Data Center (NSIDC), the Consortium for International Earth Science Information Network (CIESIN) and the US Geological Survey (USGS). We populated a prototype metadata database whose architecture facilitates the scientific and statistical investigations of geophysical parameters associated with the polar regions, allowing for data fusion from other regions and Earth science disciplines, facilitating interdisciplinary studies. The user can extract information combining the knowledge of two disparate sources of geophysical data to allow a query that would result in a useful product. Furthermore, we demonstrate the utility of allowing access to this database via the World Wide Web using an interface to the underlying Oracle database management system.